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Ensuring privacy and confidentiality of cloud data: A comparative analysis of diverse cryptographic solutions based on run time trend

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  • John Kwao Dawson
  • Twum Frimpong
  • James Benjamin Hayfron Acquah
  • Yaw Marfo Missah

Abstract

The cloud is becoming a hub for sensitive data as technology develops, making it increasingly vulnerable, especially as more people get access. Data should be protected and secured since a larger number of individuals utilize the cloud for a variety of purposes. Confidentiality and privacy of data is attained through the use of cryptographic techniques. While each cryptographic method completes the same objective, they all employ different amounts of CPU, memory, throughput, encryption, and decryption times. It is necessary to contrast the various possibilities in order to choose the optimal cryptographic algorithm. An integrated data size of 5n*102 (KB (∈ 1,2,4,10,20,40) is evaluated in this article. Performance metrics including run time, memory use, and throughput time were used in the comparison. To determine the effectiveness of each cryptographic technique, the data sizes were run fifteen (15) times, and the mean simulation results were then reported. In terms of run time trend, NCS is superior to the other algorithms according to Friedman’s test and Bonferroni’s Post Hoc test.

Suggested Citation

  • John Kwao Dawson & Twum Frimpong & James Benjamin Hayfron Acquah & Yaw Marfo Missah, 2023. "Ensuring privacy and confidentiality of cloud data: A comparative analysis of diverse cryptographic solutions based on run time trend," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-15, September.
    • Handle: RePEc:plo:pone00:0290831
    DOI: 10.1371/journal.pone.0290831
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    References listed on IDEAS

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    1. Yong-Ki Kim & Hyeong-Jin Kim & Hyunjo Lee & Jae-Woo Chang, 2022. "Privacy-preserving parallel kNN classification algorithm using index-based filtering in cloud computing," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-29, May.
    2. Reem ALmarwani & Ning Zhang & James Garside, 2020. "An effective, secure and efficient tagging method for integrity protection of outsourced data in a public cloud storage," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-47, November.
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